جست‌وجوی استاد و پژوهشگر

• Computational Materials Science
• Molecular Dynamics
• Machine Learning
• +۲ مورد دیگر

Dr. Priya Vashishta is a Professor with joint appointments in Computer Science, Materials Science, and Physics at the University of Southern California. His research integrates computational methods across multiple scales to address fundamental challenges in materials science and nanotechnology. Research focuses on multiscale simulation frameworks combining quantum molecular dynamics, machine learning interatomic potentials, and high-performance computing. Key areas include: energy storage materials, 2D materials characterization, catalytic processes, and AI-driven materials discovery. Publication trends reveal consistent development of computational methodologies: machine learning potentials for materials simulation, neural network quantum dynamics, GPU-accelerated computing, and multiscale modeling techniques. Recent work emphasizes practical applications in energy storage, nanoscale electronics, and advanced manufacturing. Research group activities include development of open-source simulation packages (PND, Allegro) and leadership in the MAGICS computational materials center. Contributions span fundamental theoretical frameworks to applied materials engineering.

Patrick Punمشاهده پروفایل

دانشیار

• Applied Mathematics
• Financial Mathematics
• Quantitative Finance
• +۷ مورد دیگر

Patrick Pun is an Associate Professor in the Division of Mathematical Sciences at Nanyang Technological University (NTU), Singapore, serving as Assistant Chair (MSc Programs) and Director of the MSc in FinTech program within the School of Physical and Mathematical Sciences. His academic journey includes a Ph.D. and M.Phil. from the Chinese University of Hong Kong (CUHK) and a B.Sc. from Nankai University. Professor Pun’s research focuses on the intersection of applied mathematics and finance, with methodologies spanning stochastic controls, nonlinear partial differential equations (PDEs), robust optimization, and machine learning. His work addresses challenges in portfolio optimization, derivatives pricing, risk management, and financial data analysis. He has also contributed to interdisciplinary areas such as nanoparticle characterization and epidemiological modeling (e.g., during the COVID-19 pandemic). Award-winning scholar, Pun holds the 2016 Nicola Bruti Liberati Prize from the Bachelier Finance Society and the CUHK Young Scholars Thesis Award. He is an ad-hoc reviewer for leading journals including Automatica , SIAM Journal on Financial Mathematics , and Quantitative Finance . His professional roles include membership in the Academic Council of the Global Digital Economy Forum. Pun’s teaching and academic leadership include oversight of master’s programs and curriculum development. His research outputs emphasize innovative solutions to time-inconsistent problems, high-dimensional financial data challenges, and the integration of machine learning with traditional financial models. His recent work explores transformer-based generative models, quantum algorithms for financial PDEs, and reinforcement learning applications in portfolio management.

Martin Herdegenمشاهده پروفایل

دانشیار

• Mathematical Finance
• Probability Theory
• Stochastic Processes
• +۵ مورد دیگر

Martin Herdegen is a Reader in Financial Mathematics at the Department of Statistics, University of Warwick. He previously served as a postdoc at ETH Zürich under Johannes Muhle-Karbe and holds a PhD in Mathematics from ETH Zürich (2014) under Martin Schweizer. His research focuses on equilibrium theory, utility maximization, stochastic processes, and risk measures, with applications to financial bubbles and market microstructure. Herdegen’s academic career includes supervising multiple PhD students (e.g., Florian Gutekunst, Andreea Popescu) and postdocs (e.g., Nazem Khan). His research group explores topics like ρ-arbitrage, recursive utility, and liquidity provision under adverse conditions. He has contributed to foundational work on strict local martingales and their implications for financial markets. Publications span leading journals such as Finance and Stochastics , Mathematical Finance , and Annals of Applied Probability , addressing equilibrium models with transaction costs, optimal investment strategies, and risk measurement techniques. His work frequently integrates stochastic analysis and control theory to solve practical finance problems. Herdegen’s research also intersects with reinforcement learning applications in trading, as seen in the Mbt-gym framework for limit order book simulations. His contributions emphasize rigorous mathematical foundations while addressing real-world market frictions and liquidity dynamics.

Mohamed-Lamine Messaiمشاهده پروفایل

استاد

• Key Management in IoT Networks
• Cybersecurity
• Networking Security
• +۳ مورد دیگر

Mohamed-Lamine Messai is a Professor at Université Lyon 2, France, specializing in cybersecurity, IoT security, and networking. He teaches courses such as Computer Security, Hacking Labs, and Cryptography, and is actively involved in research projects like ROMANCE (knowledge graph modeling for organizational vulnerabilities) and GLADIS (graph-based intrusion detection). His work focuses on key management systems, resource-constrained networks, and AI-driven cybersecurity solutions. He has supervised numerous PhD and master's students, including Abel Oroke, Floribert Katembo, and Sami Bettayeb. His research spans IoT security, blockchain applications, and graph neural networks for threat detection. He frequently presents at international conferences and workshops, including GRASEC, ARES, and IWCMC. Current projects include Federated Learning with Hybrid Encryption for APT detection and autonomous bio-inspired 5G deployment strategies. Key Projects: ROMANCE, GLADIS, CyberSecGraph, DATACAP Teaching: M1/M2 courses in Computer Security, IoT, Networking, and Distributed Computing Lab Affiliations: Involved in L3i lab activities and Open Days at the University of La Rochelle

Chew Ek Pengمشاهده پروفایل

دانشیار

• Port Logistics
• Maritime Transportation
• Simulation Optimization
• +۳ مورد دیگر

Chew Ek Peng is an Associate Professor and Deputy Head at the Institute of Operations Research and Analytics (IORA), part of the National University of Singapore’s Smart Nation Research Cluster. His research focuses on optimizing port logistics, maritime transportation systems, and inventory management through advanced simulation techniques and data-driven methodologies. His work spans critical areas such as automated port operations, simulation-optimization frameworks, and stochastic systems analysis. Notably, he develops solutions for challenges like AGV scheduling, container relocation problems, and intermodal terminal design. His research integrates machine learning (e.g., hybrid neural networks) with traditional operations research methods. Recent publications highlight contributions to electric vehicle sustainability in carsharing systems, multi-agent reinforcement learning for AGV recharging, and facility location under random utility models. His work emphasizes practical applications in smart logistics, disaster response optimization, and supply chain resilience. Chew Ek Peng collaborates on projects like digital twin validation frameworks and modular simulation pipelines for residential energy modeling. His research has been applied to real-world scenarios such as Singapore’s construction demand forecasting and pandemic impact analyses using modified SEIR models.

• Computer Architecture
• Embedded Systems
• High-Performance Computing
• +۹ مورد دیگر

Zapater Sancho Marina is an Associate Professor at the ReDS Institute (Institute of Reconfigurable and Embedded Digital Systems) within the School of Engineering and Management Vaud (HEIG-VD), part of the University of Applied Sciences and Arts Western Switzerland (HES-SO). She holds dual master's degrees in Electronic and Telecommunication Engineering from Universitat Politècnica de Catalunya (2010) and a PhD in Computer Science from Universidad Politécnica de Madrid (2015). Her career includes postdoctoral work at EPFL (2016-2020) and assistant professorship at Universidad Complutense de Madrid (2015-2016). Education BSc & MSc in Electronic Engineering (UPC 2010) PhD in Computer Science (UPM 2015) Research Focus spans cross-layer optimization of heterogeneous architectures for performance and energy efficiency, with emphasis on: Embedded systems (IoT/edge computing) High-performance compute architectures Analog in-memory computing for AI Thermal/power management in 3D chips Cloud-edge AI workload orchestration Publication Trends show expertise in RISC-V simulation frameworks, analog computing tiles for CNNs, virtual memory redesign, and AI-driven cloud performance prediction. Her recent work explores thermal-aware 3D chip management, hybrid-cache reliability optimization, and open-source teaching platforms for radio theory. Awards include a Spanish government PhD fellowship. She has led 4 European H2020 projects since 2016 and currently serves as PI for 4 industrial collaborations (Facebook/Intel/Huawei), Innosuisse projects, and HES-SO initiatives. Labs & Teams include the ReDS Institute, EPFL's Embedded Systems Laboratory, and collaborations with Yale/Edinburgh. She co-developed the ALPINE simulation framework and SO3 operating system modifications for Midgard project validation.

Liming Chenمشاهده پروفایل

استاد

• Computer Vision
• Pattern Recognition
• 3D Face Analysis
• +۲ مورد دیگر

Liming Chen is a Full Professor and Director of the Department Mathématiques - Informatique at École Centrale de Lyon, Université de Lyon. As a member of the Laboratoire d'Informatique en Image et Systèmes d'Information (LIRIS, UMR 5205), his research spans computer vision, pattern recognition, and multimedia computing. His extensive research portfolio includes 3D face analysis, image/video categorization, affect analysis, and biometric systems. He has led significant projects such as ANR 3D Face Analyzer, ANR Videosense, ANR Omnia, and ANR FAR3D, focusing on advanced recognition systems and multimodal analysis. Professor Chen supervises multiple PhD students and has developed open-source tools for 3D model processing. His work has received recognition through best performance awards at international competitions including SHREC 2011 (3D face recognition) and ImageCLEF 2011 (photo annotation).

Benjamin D. Wandeltمشاهده پروفایل

استاد پژوهشی

• Cosmology
• Theoretical Astrophysics
• Data Science
• +۳ مورد دیگر

Benjamin D. Wandelt is a Research Professor at Johns Hopkins University with joint appointments in the Department of Physics and Astronomy and the Department of Applied Mathematics and Statistics. As a cosmologist and data scientist, he studies fundamental physics through astronomical observations using AI/ML and statistical inference. His research connects theoretical astrophysics with large-scale astronomical data to explore the cosmological framework. Research Focuses: Cosmological parameter estimation from supernova and CMB data Dark energy constraints through large-scale structure Machine learning applications in astrophysical inference Statistical methods for cosmological data analysis Major Honors: Gruber Prize in Cosmology (2018) Friedrich Wilhelm Bessel Prize Fellow of the American Physical Society Fellow of the International Association of Astrostatisticians

Nuria Pelechano Gomezمشاهده پروفایل

استاد

• Character Animation
• Virtual Humans
• Immersive Environments
• +۳ مورد دیگر

Nuria Pelechano Gomez is a Professor at the Department of Computer Science, Polytechnic University of Catalonia (UPC), affiliated with the Barcelona School of Informatics (FIB). She leads research in the ViRVIG group (Visualització, Realitat Virtual i Interacció Gràfica), focusing on character animation, virtual humans, and crowd simulation. Her work emphasizes immersive environments and real-time rendering applications. Education: PhD in Computer Science from UPC (thesis: 'Real-Time Avatar Animation Synthesis in Virtual Reality' by Yun, H. 2023). Research interests include motion capture, crowd behavior modeling, and VR/AR applications. Recent work explores sparse data reconstruction, avatar customization, and disease spread simulations. She has contributed to over 140 publications, including high-impact journals like ACM Transactions on Graphics and Computer Graphics Forum. Awards include 'Best Teaching Assistant' in 2005 and 2006. Active in academic governance, she serves on program committees for top conferences (e.g., Eurographics, SIGGRAPH Motion in Games). Her projects (e.g., MPACT, XR4ED) are funded by national and EU grants. Labs/Teams: ViRVIG Group at UPC, collaborating with international institutions on projects like CAVAST (Crowd Animation, Visualization, and Simulation Testbed).

Raffaella Bernardiمشاهده پروفایل

دانشیار

• Computational Linguistics
• Natural Language Processing
• Artificial Intelligence
• +۶ مورد دیگر

Raffaella Bernardi is an Associate Professor at the University of Trento, affiliated with the Department of Information Engineering and Computer Science (DISI) and the Center for Mind/Brain Science (CIMeC). She holds a PhD in Logic and Language from Utrecht University (2002) and has held academic positions at the Free University of Bozen-Bolzano and the University of Trento. Her research focuses on computational linguistics, formal semantics, distributional semantics, and multimodal models, with a particular emphasis on integrating vision and language systems. Education: PhD in Logic and Type Theory (2002, Utrecht University), Master's in Philosophy (1994, University of Chieti), and High School Diploma (1990). Research Interests: Natural Language Processing, Categorial Type Logic, Compositional Distributional Semantics, Visual Dialogues, and Multimodal Reasoning. She has pioneered work on grounding language in visual contexts and has contributed to key projects like the ERC-funded COMPOSES initiative. Grants & Leadership: Principal Investigator on EU projects (e.g., CACAO, Galateas) and member of the ERC COMPOSES team. She chairs conferences like ACL and EMNLP and serves on editorial boards for journals like the Journal of Logic, Language, and Information. Labs: Leads the LaVi (Language and Vision) research group and collaborates with the CLIC Lab. Active in organizing workshops on Formal Grammar and Vision-Language Integration.

Kristofer Reyesمشاهده پروفایل

دانشیار

• Applied Mathematics
• Computational Chemistry
• Materials Science
• +۶ مورد دیگر

Kristofer Reyes is an Associate Professor in the Department of Materials Design and Innovation at the University at Buffalo (School of Engineering and Applied Sciences). His research focuses on computational and statistical methods applied to materials science, particularly in developing machine learning frameworks for small-data regimes. He leads the Computational and Statistical Material Science (CSMS) Lab, which emphasizes Bayesian models, reinforcement learning, and fusion of physics-based knowledge. Education: PhD in Applied and Interdisciplinary Mathematics, University of Michigan, 2013 BS in Computer Science and Mathematics, Purdue University, 2004 Research Interests: Reyes' work bridges computation, mathematics, and materials science. Key areas include: - Autonomous experimentation systems (e.g., self-driving fluidic labs) - Bayesian optimization and decision-making under uncertainty - Machine learning for nanomaterials synthesis (e.g., perovskite nanocrystals) - Integration of physics-informed models in AI workflows - High-cost experimental design optimization Recent Achievements: SMARTDOPE paper awarded 'Best in Advanced 2023' Developed AlphaFlow for autonomous chemical synthesis Pioneered 'self-driving labs' for materials discovery Labs & Teams: CSMS Lab (134 Bell Hall) focuses on problem-fluent models for materials research. Current projects include quantum circuit optimization, bio-chemical pathway modeling, and sustainable nanomanufacturing. Collaborates with industry and national labs on AI-driven materials development.

Xudong Fanمشاهده پروفایل

استادیار

• Urban Water Systems
• Resilience and Sustainability
• Artificial Intelligence
• +۳ مورد دیگر

Xudong (Andrew) Fan is an Assistant Professor in the Department of Civil, Structural and Environmental Engineering at the University at Buffalo (UB), part of the School of Engineering and Applied Sciences. His research focuses on urban water systems, resilience and sustainability in water infrastructure, and applications of artificial intelligence in complex networks. He holds a PhD in Civil and Environmental Engineering from Case Western Reserve University (2022), an MS in Structural Engineering from Tianjin University (2017), and a BS in Civil Engineering from Central South University (2014). Dr. Fan's work integrates advanced computational methods with civil engineering challenges, particularly in enhancing infrastructure resilience through AI-driven approaches. His research spans smart city technologies, climate change adaptation, and bio-based solutions for soil stabilization and frost heave mitigation. Notable contributions include developing graph neural networks for spatially embedded network analysis and machine learning models for predicting water infrastructure failures. His publications reflect a strong emphasis on interdisciplinary problem-solving, combining data science with traditional engineering domains. While no specific awards are listed, his prolific output since 2020 highlights his active engagement in cutting-edge research areas such as AI for water systems and sustainable materials innovation.

• Distributed Systems
• Blockchain Technology
• Graph Neural Networks
• +۵ مورد دیگر

Hans-Arno Jacobsen is a Professor at Technische Universität München (Faculty of Computer Science, Germany) and the University of Toronto (Department of Electrical and Computer Engineering, Canada). His research spans distributed systems, blockchain technology, and machine learning for energy systems. Research Interests : Blockchain consensus algorithms, federated learning, graph neural networks, quantum computing applications, and energy-efficient distributed systems. Publication Trends : Recent work focuses on decentralized consensus in blockchains, energy-aware language model inferencing, quantum chemistry simulations, and graph neural network scalability. Collaborations : Regularly works with Ruben Mayer, Shashank Motepalli, and Gengrui Zhang on blockchain and machine learning projects.

Jiong Tangمشاهده پروفایل

استاد

• System Dynamics
• Control
• Sensing and Automation
• +۷ مورد دیگر

Jiong Tang is a Pratt & Whitney Chair Professor in Design and Manufacturing at the University of Connecticut , where he also serves as Co-Director of the Management and Engineering for Manufacturing Program . He received his B.S. and M.S. in Applied Mechanics from Fudan University, China (1989 and 1992), and his Ph.D. in Mechanical Engineering from Pennsylvania State University (2001). Prior to joining UConn, he worked at the GE Research Center as a research engineer. Research Interests : System dynamics, control theory, smart materials, vibration suppression, uncertainty propagation, computational intelligence, and multi-physics system modeling. Current Projects : Digital twin development for aerospace materials, physics-informed machine learning in manufacturing, adaptive metasurface design, and optimization of cooperative robotics. Methodological Focus : Combines Bayesian deep learning , Gaussian process metamodeling , transformer-based architectures , and multi-fidelity data fusion for industrial applications. His work emphasizes smart sensing , electromechanical integration , and uncertainty-robust inverse analysis . Collaboration : Research funded by federal agencies and industrial partners , with particular emphasis on aerospace and manufacturing technologies. His recent publications highlight generative adversarial networks for defect detection , piezoelectric metamaterials , and physics-guided neural network architectures across mechanical, structural, and composite systems.

Johann Kastnerمشاهده پروفایل

استاد

• Computed Tomography
• Materials Science
• Non-Destructive Testing
• +۳ مورد دیگر

Johann Kastner is a Professor at Upper Austria University of Applied Sciences, leading the Research Center Wels Computed Tomography R&D-Headquarters. He is affiliated with Centers of Excellence in Automotive/Mobility, Energy, and Smart Production. His research focuses on advanced materials characterization using X-ray computed tomography (X-CT), with applications in non-destructive testing, composite materials, and biomedical engineering. Key research areas include porosity analysis in carbon fiber reinforced polymers, phase contrast imaging, and additive manufacturing. He has led over 10 projects, including the EU-funded xCTing initiative (2021–2025) and the X-PRO project (2020–2024), emphasizing industrial CT applications and cross-virtuality data analysis. His work spans 335+ publications, with notable contributions to XCT-based defect detection, material microstructure analysis, and AI-driven image segmentation. Collaborations include COMET K Projects and FTI-Structurförderung grants. He has advised 2 PhD students and actively participates in international conferences and workshops. Laboratory facilities include state-of-the-art XCT systems for 3D microstructural analysis, Talbot-Lau grating interferometry, and augmented reality visualization tools for industrial applications.